Balancing national economic policy outcomes for sustainable development

The 2030 Sustainable Development Goals (SDGs) aim at jointly improving economic, social, and environmental outcomes for human prosperity and planetary health. However, designing national economic policies that support advancement across multiple Sustainable Development Goals is hindered by the complexities of multi-sector economies and often conflicting policies. To address this, we introduce a national-scale design framework that can enable policymakers to sift through complex, non-linear, multi-sector policy spaces to identify efficient policy portfolios that balance economic, social, and environmental goals. The framework combines economy-wide sustainability simulation and artificial intelligence-driven multiobjective, multi-SDG policy search and machine learning. The framework can support multi-sector, multi-actor policy deliberation to screen efficient policy portfolios. We demonstrate the utility of the framework for a case study of Egypt by identifying policy portfolios that achieve efficient mixes of poverty and inequality reduction, economic growth, and climate change mitigation. The results show that integrated policy strategies can help achieve sustainable development while balancing adverse economic, social, and political impacts of reforms.


Supplementary
. Evolution of the hypervolume with five random seeds for each of the examined integrated policy strategies for the Egyptian economy: a transfer distribution and transfer level, b transfer distribution, transfer level, and income taxes, c Producer taxes/subsidies, transfer distribution, transfer level, and income taxes, and d Producer taxes/subsidies, sales taxes, transfer distribution, transfer level, and income taxes.
Supplementary Figure 8. Performance of the Random Forest Regression models with the training and testing data based on maximum tree depths ranging from 1 to 50. a-l performance of the machine learning models in predicting sustainability metrics. 80% of the data was used for training and 20% of the data was used for testing. A tree depth was selected for each model to avoid over-and/or under-fitting the data. Where RGDP is the total discounted real GDP, 0 is the deflated market price of commodity c, ,ℎ is the quantity of commodity c consumed by household group h, 0 , is the price of commodity c produced by domestic activity a, , ,ℎ is the quantity of commodity c produced by the domestic activity a and consumed by household group h, is the investment demand of commodity c, is the stock change of commodity c, is the quantity of government consumption of commodity c, 0 is the deflated export price of commodity c, is the exchange rate, is the quantity of export of commodity c, 0 is the deflated import price of commodity c, is the quantity of import of commodity c, is the discount rate, and is the simulation time step.

(Supplementary Equation 2)
Where is the total discounted net real income of urban households, is the direct income tax rate on urban household urb, is the income deflator of urban household urb, is the income of urban household urb, is the discount rate, and is the simulation time step.

(Supplementary Equation 3)
Where is the total discounted net real income of rural households, is the direct income tax rate on rural household rur, is the income deflator of rural household rur, is the income of rural household rur, is the discount rate, and is the simulation time step.
Where is the mean overall Gini index, HQ is a set of household groups classified by income quintiles, is the income of household group i, is the income of household group j, ̅̅̅ is the mean income of household groups classified by income quintiles, n is the number of household groups classified by income quintiles, is the simulation time step, and T is the total number of simulation years. Where is the mean rural mean Gini index, HQRUR are the rural household groups classified by income quintiles, is the income of rural household group i, is the income of rural household group j, ̅̅̅ is the mean income of rural household groups classified by income quintiles, n is the number of rural household groups classified by income quintiles, is the simulation time step, and T is the total number of simulation years.

(Supplementary Equation 7)
Where is the quantity consumed of the CO2 emitting commodity ec, is the emission coefficient of the CO2 emitting commodity ec, and is the simulation time step.